Section 3: Create a Custom Newsfeed
3.1 Creating a Supervised Training Set
with the Pocket App
To create a model, we have to first have a training dataset.
We will use the pocket app for this.
·
Install the pocket chrome extension.
·
Use the pocket API to retrieve stories.
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3.2 Using the embed.ly API to Download
Story Bodies
You can't move forward with just the URLs of the stories.
You would need the full article. So let's check out how to do that in this
video.
·
Sign up for embed.ly API access.
·
Feed plain text to the model.
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3.3 Natural Language Processing Basics
Machine learning models work on numerical data. So we will
need to transform our text into numerical data using NLP.
·
Convert the corpus into a BOW representation.
Remove stop words.
·
Use the tf-idf algorithm. Convert the training
set into a tf-idf matrix.
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3.4 Support Vector Machines
You will learn about the linear support vector machine in
this video. The SVM algorithm separates data points linearly into classes.
·
Feed the tf-idf matrix into the SVM.
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3.5 IFTTT Integration with Feeds, Google
Sheets, and E-mail
We have provided a training dataset. But we also need a
stream of articles as a testing dataset to run our model against.
·
Set up news feeds and Google sheets.
·
Pull down articles using a Python library.
·
Make changes if necessary and rebuild the model.
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3.6 Setting Up Your Daily Personal
Newsletter
It would make life easier if you get a personalized e-mail
of your stories, right? So you will learn how to do that in this video.
·
Create a recipe. Receive a web request and
create a trigger.
·
Generate a script that will send us articles
daily.
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Section 4
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